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Smoke detection in video based on motion and contrast

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Smoke detection in video based on motion and contrast

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An efficient smoke detection algorithm on color video sequences obtained from a stationary camera is proposed. Our algorithm considers dynamic and static features of smoke and composed of basic steps: preprocessing; slowly moving areas and pixels segmentation in a current input frame based on adaptive background subtraction; merge slowly moving areas with pixels into blobs; classification of the blobs obtained before.

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Nội dung Text: Smoke detection in video based on motion and contrast

Journal of Computer Science and Cybernetics, V.28, N.3 (2012), 195205<br /> <br /> SMOKE DETECTION IN VIDEO BASED ON MOTION AND CONTRAST<br /> N. BROVKO1 , R. BOGUSH1 , S. ABLAMEYKO2<br /> 1 Polotsk State University, 29, Blokhin str., Novopolotsk, Belarus<br /> 2 Belarusian State University, 4, Nezavisimosti av., Minsk, Belarus<br /> <br /> Tóm t t. B i b¡o · xu§t mët thuªt to¡n húu hi»u ph¡t hi»n khâi trong video m u tø m¡y quay<br /> camera t¾nh. Thuªt to¡n xem x²t c¡c °c tr÷ng ëng v  t¾nh cõa khâi bao gçm c¡c b÷îc cì b£n: Ti·n<br /> sû l½; C¡c mi·n di chuyºn chªm v  c¡c ph¥n o¤n £nh iºm trong khung dú li»u nhªp düa tr¶n kh§u<br /> trø th½ch nghi; Hñp nh§t c¡c mi·n dàch chuyºn chªm vîi c¡c iºm £nh th nh c¡c giåt n÷îc; Ph¥n<br /> lo¤i c¡c giåt n÷îc. B i b¡o ¢ sû döng ph÷ìng ph¡p kh§u trø th½ch nghi tr¶n tøng giai o¤n ph¡t<br /> triºn khâi. Ph¥n lo¤i c¡c giåt n÷îc di ëng düa tr¶n t½nh to¡n c¡c dáng quang håc, tr¶n sü ph¥n t½ch<br /> t÷ìng ph£n Weber v  câ t½nh ¸n h÷îng khâi lan täa. Phèi hñp gi¡m s¡t c¡c h¼nh £nh video thªt<br /> ÷ñc sû döng º ph¡t hi»n khâi. C¡c k¸t qu£ thüc nghi»m công ÷ñc ÷a ra.<br /> Abstract. An efficient smoke detection algorithm on color video sequences obtained from a stationary<br /> camera is proposed. Our algorithm considers dynamic and static features of smoke and composed of<br /> basic steps: preprocessing; slowly moving areas and pixels segmentation in a current input frame based<br /> on adaptive background subtraction; merge slowly moving areas with pixels into blobs; classification<br /> of the blobs obtained before. We use adaptive background subtraction at a stage of moving detection.<br /> Moving blobs classification is based on optical flow calculation, Weber contrast analysis and takes<br /> into account primary direction of smoke propagation. Real video surveillance sequences are used for<br /> smoke detection with utilization our algorithm. A set of experimental results are presented in the<br /> paper.<br /> Keywords. smoke detection, video sequences, background subtraction, Weber contrast analysis<br /> 1.<br /> <br /> INTRODUCTION<br /> <br /> Reliable and early fire detection on open spaces, in buildings, in territories of the industrial<br /> enterprises are an important feature to make any system of fire safety. Traditional fire detectors<br /> which have been widely applied in the buildings are based on infrared sensors, optical sensors,<br /> or ion sensors that depend on certain characteristics of fire, such as smoke, heat, or radiation.<br /> Such detection approaches require a position of sensor in very close proximity to fire or smoke<br /> and often give out false alarms. Thus they may be not reliable and cannot be applied into<br /> open spaces and larger areas.<br /> Effective systems for early fire detection into open spaces are using technologies such as<br /> image and video processing [1, 2], radio-acoustic sounding (RASS) [3], light detection and<br /> ranging (LIDAR) [4]. Due to the rapid developments in digital camera technology and video<br /> processing techniques currently intelligent video surveillance systems are installed in various<br /> public places for monitoring. Therefore there is a noticeable trend to use such systems for<br /> <br /> 196<br /> <br /> N. BROVKO, R. BOGUSH, S. ABLAMEYKO<br /> <br /> early fire detection with special software applied [5]. Smoke detection is rather for fire alarm<br /> systems when large and open areas are monitored, because the source of the fire and flames<br /> cannot always be captured. Whereas smoke of an uncontrolled fire can be easily observed by<br /> a camera even if the flames are not visible. This results in early detection of fire before it<br /> spreads around.<br /> Motion and color are two usually used important features for detecting smoke on the<br /> video sequences. Motion information provides a key as the precondition to locate the possible<br /> smoke regions. The algorithm of background subtraction is traditionally applied to movement<br /> definition in video sequence [6, 8]. Common technique is using adaptive Gaussian Mixture<br /> Model to approximate the background modeling process [6, 7].<br /> The existing algorithms of smoke detection combine various smoky properties based on<br /> classifiers. In the paper [6], the energy ratio and the color blending have been combined using<br /> a Bayesian classifier to detect smoke on the scene. The algorithm in paper [5] is mainly based on<br /> determining the edge regions whose wavelet sub band energies decrease with time and wavelet<br /> based contour analysis of possible flame regions. These regions are then analyzed along with<br /> their corresponding background regions with respect to their RGB and chrominance values.<br /> In [9], optical flow calculation is applied to detection of movement of a smoke. Lacks of<br /> the present approach are high sensitivity to noise and low performance. Algorithms based on<br /> color and dynamic characteristics of a smoke are applied to classify the given moving blobs.<br /> In [10] the algorithm comparative evaluation of the histogram-based pixel level classification<br /> is considered. Based on this algorithm the training set of video sequences on which there is<br /> a smoke is applied to the analysis. In [11] the algorithm uses estimated motion orientation<br /> with accumulation intensity for disturbance of artificial lights and non-smoke moving objects<br /> elimination.<br /> Color information is also used to identify smoke in video. Smoke color changes at the<br /> different stages of ignition and depending on a burning material is distributed in a range from<br /> almost transparent white to saturated gray and black. In [6] decrease in value of chromatic<br /> components U and V of color space YUV is estimated.<br /> Image regions containing smoke are characterized with a dynamic texture (changing texture of an image over time) [12]. In [13] a model of the instantaneous motion maps allows<br /> to track motion textures using the conditional Kullback-Leibler divergence between mixedstate probability densities, which allows to estimate the position using a statistical matching<br /> approach.<br /> In this paper, we propose an algorithm for smoke detection on color video sequences obtained from a stationary camera. Our algorithm consists of the following steps: preprocessing;<br /> slowly moving areas and pixels segmentation in a current input frame based on adaptive background subtraction; merge slowly moving areas with pixels into blobs; classification of the<br /> blobs obtained before. We use adaptive background subtraction at a stage of moving detection. Moving blobs classification is based on optical flow calculation, Weber contrast analysis<br /> and takes into account primary direction of smoke propagation.<br /> <br /> 2.<br /> <br /> ALGORITHM DESCRIPTION<br /> <br /> The proposed algorithm uses motion and contrast as the two key features for smoke detection. Motion is a primary sign and used at the beginning for extraction from a current frame<br /> <br /> SMOKE DETECTION IN VIDEO BASED ON MOTION AND CONTRAST<br /> <br /> 197<br /> <br /> of candidate areas. In addition we consider a direction of smoke distribution the movement<br /> estimation based on the optical flow is applied. The relation of smoke intensity to background<br /> intensity above than at objects with similar behavior, such as a fog, shadows from slowly<br /> moving objects and patches of light. Therefore, contrast calculated with Weber formula is a<br /> good distinctive sign for a smoke. The algorithm is a group of the following modules as showed<br /> in Figure 2.1.<br /> Consecutive frames It−2 , It−1 , It and It−1 obtained from the stationary video surveillance<br /> camera are entered to an input of the preprocessing block. This block carries out some transformations, which improve contrast qualities of the input frames and reduce calculations. Then<br /> adaptive background subtraction is applied to extract from the frame<br /> <br /> It−1 of slowly moving<br /> areas and pixels of the so-called foreground. The background subtraction adaptive algorithm<br /> considers that a smoke gradually is mixed to a background. Then the connected components<br /> analysis is used to clear the foreground noise and to merge the slowly moving areas with pixels<br /> into blobs. The received connected blobs are transferred into the classification block for Weber<br /> contrast analysis. At the same times the connected blobs are entered to an input of the block<br /> for optical flow calculation. Finally, the classification block processes the information to obtain<br /> the final result of smoke detection.<br /> 2.1.<br /> <br /> Frame preprocessing<br /> <br /> The preprocessing block applies some methods of image processing, which increase the<br /> performance of the proposed detection algorithm and reduce false alarms. Frame preprocessing<br /> block comprises three steps: grayscale transformation, histogram equalization and the discrete<br /> wavelet of the current input frame. Cameras and image sensors must usually deal not only with<br /> the contrast on a scene, but also with the image sensors exposure to the resulting light on that<br /> scene. Histogram equalization is a most commonly used method for improving contrast image<br /> characteristics. To resize the image and to remove high frequencies on horizontal, vertical and<br /> diagonal details the discrete wavelet transform to Haar basis is applied. Wavelet transform to<br /> Haar basis is the simplest and the fastest [14] algorithm that is important for systems of video<br /> processing. Figure 2.2 shows the results for this step of algorithm.<br /> 2.2.<br /> <br /> Slowly moving areas and pixels segmentation<br /> <br /> In the course of the distribution a smoke is being gradually blended to the background.<br /> Our adaptive algorithm of background subtraction considers this characteristic of a smoke<br /> and is based on the ideas of [7, 15]. A background image<br /> estimated from the image frame<br /> <br /> It−1<br /> <br /> Bt at time instant t is recursively<br /> and the background image Bt−1 of the video as follows<br /> <br /> [15]:<br /> <br /> Bt (x, y) =<br /> where<br /> <br /> (x, y)<br /> <br /> αBt−1 (x, y) + (1 − α)Tt−1 (x), if (x, y)<br /> Bt−1 (x, y), if (x, y) is stationary<br /> <br /> represent a pixel video frame and<br /> <br /> α<br /> <br /> is moving<br /> <br /> is a adaptation parameter between 0 and 1.<br /> <br /> As the area of a smoke frame by frame grows slowly that the pixels belonging to a smoke,<br /> quickly did not fix in a background, value<br /> At the initial moment of time<br /> <br /> α should close to 1.<br /> B0 (x, y) = I0 (x, y). Pixel (x, y)<br /> <br /> the following condition is satisfied [15]:<br /> <br /> belongs to moving object if<br /> <br /> 198<br /> <br /> N. BROVKO, R. BOGUSH, S. ABLAMEYKO<br /> <br /> Figure 2.1.<br /> <br /> Flow chart of our proposed algorithm<br /> <br /> Figure 2.2. The current frame (a), histogram equalization (b) and the Haar transform of the<br /> current frame (c)<br /> <br /> 199<br /> <br /> SMOKE DETECTION IN VIDEO BASED ON MOTION AND CONTRAST<br /> <br /> (|It (x, y) − It−1 (x, y)| > Tt−1 (x, y)) & (|It (x, y) − It−2 (x, y)| > Tt−1 (x, y)),<br /> where It−2 (x, y), It−1 (x, y), It (x, y) values of intensity of pixel<br /> and t respectively;<br /> <br /> Tt (x, y)<br /> <br /> is adaptive threshold for pixel<br /> <br /> Tt (x, y) =<br /> <br /> (x, y)<br /> <br /> (x, y) at time instant t − 2, t − 1<br /> <br /> at time instant<br /> <br /> t<br /> <br /> calculated as follows:<br /> <br /> αTt−1 (x, y) + (1 − α)(5 × |It−1 (x, y) − Bt−1 (x, y)|), if (x, y)<br /> Tt−1 (x, y), if (x, y) is stationary.<br /> <br /> is moving<br /> <br /> At the initial moment of time<br /> <br /> T0 (x, y) = const > 0.<br /> Accurate separating of a foreground object from the background is the main task of digital<br /> matting. Porter and Duff [17] introduced the blending parameter (so-called alpha channel) as<br /> a solution of this problem and a mean to control the linear combination of foreground and<br /> background components. Mathematically the current frame It+1 is modeled as a combination<br /> Ft+1 and background Bt components using the blending parameter β :<br /> <br /> of foreground<br /> <br /> It+1 (x, y) = βFt+1 (x, y) + (1 − β)bt (x, y).<br /> For opaque objects value of<br /> <br /> β<br /> <br /> is equal to 1, for transparent objects value of<br /> <br /> 0 and for the semitransparent objects, such as smoke, value of<br /> <br /> β<br /> <br /> β<br /> <br /> is equal to<br /> <br /> lays in a range from 0 to 1.<br /> <br /> As it is shown further in this section we have experimentally established the optimum value<br /> for<br /> <br /> β,<br /> <br /> to be equal to 0.38.<br /> <br /> So, as soon as we have obtained<br /> <br /> It+1<br /> <br /> and set<br /> <br /> β<br /> <br /> Bt component on background update step, current frame<br /> to 0.38, we can estimate the foreground component Ft+1 . Then we apply the<br /> <br /> threshold processing to receive the binary foreground<br /> <br /> Fbin =<br /> <br /> Fbin :<br /> <br /> 1, if (Ft+1 > 245)<br /> 0, otherwise.<br /> <br /> The figure 2.3 shows the results of adaptive background subtraction and threshold of<br /> foreground component<br /> <br /> Ft+1 .<br /> <br /> Figure 2.3. The current frame<br /> component<br /> <br /> Ft+1<br /> <br /> It+1<br /> <br /> (a), the background component<br /> <br /> (c) the noisy threshold foreground<br /> <br /> Fbin<br /> <br /> At the current step of algorithm we have 2 parameters<br /> estimated. Optimum values of<br /> <br /> α and β<br /> <br /> Bt<br /> <br /> (b), the foreground<br /> <br /> (d)<br /> <br /> α<br /> <br /> and<br /> <br /> β , which are necessary to be<br /> <br /> can be estimated using receiver operating characteristic<br /> <br />
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